Prof. dr. Dimitri Solomatine

Biography

Prof. Dimitri Solomatine received his PhD from the Institute of Systems Analysis (Russian Academy of Sciences) in 1984 and he is with UNESCO-IHE Institute for Water Education (Delft) from 1990; from 2006 he is the head of the Hydroinformatics Chair group. His research interests include hydroinformatics, integration of models and remote sensing data, modelling, optimization, systems engineering, analysis of models uncertainty, computational intelligence, internet-based computing and decision support. He participated in a number of large-scale research and educational projects, has published over 170 papers, chapters in books and conference proceedings, co-edited several special issues of journals, a book, regularly organizes special sessions on hydroinformatics. He is an associate editor of the Journal of Hydroinformatics and of the Hydrology and Earth System Sciences, serves on the International Scientific Committee of all hydroinformatics conferences since 1994. He is the co-founder and chairman of the sub-division on Hydroinformatics of the European Geosciences Union. 

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Curriculum Vitae

Master in Hydroinformatics (specialisation of the Water Science and Engineering programme)

Master in Flood Risk Management (EU-funded Erasmus Mundus programme)

Chair group on Hydroinformatics

Hydroinformatics research projects

Publications

Books

• D.P. Solomatine Mathematical and program realization of interactive structural modelling system. VNIISI, Russian Academy of Sciences, Moscow, 1982 (in Russian).

Edited volumes, special journal issues

• Practical Hydroinformatics: computational intelligence and technological developments in water applications, Edited Volume by Springer (B. Abrahart, L. See, D.P. Solomatine, eds.), 2008.
• Uncertainty in flood risk management. Special Issue of the J. River Basin Management, 2008, vol. 6(2) (J. Hall, D.P. Solomatine, eds).
• Data Driven Modelling and Evolutionary Optimization for River Basin Management. Special Issue of the Journal of Hydroinformatics, 2008, vol 10(1) (A. Ostfeld, D.P. Solomatine, eds).
• Hydroinformatics: computational intelligence and technological developments in hydrologic applications. Special Issue of the Hydrological Sciences Journal, 2007 (B. Abrahart, L. See, D.P. Solomatine, E. Toth, eds).
• Data-driven approaches, optimization and model integration: hydrological applications, Special Issue of the Hydrology and Earth Systems Sciences, 2007 (B. Abrahart, L. See, D.P. Solomatine, E. Toth, eds).
• Computational Intelligence in Earth and Environmental Sciences. Special Issue of the Neural Networks Journal, 2007, vol. 20(4), (V. Cherkassky, W. Hsieh, V. Krasnopolsky, D.P. Solomatine, J. Valdes, eds.).
• Earth Sciences and Environmental Applications of Computational Intelligence. Special Issue of the Neural Networks Journal, 2006, vol. 19(2), (V. Cherkassky, V. Krasnopolsky, D.P. Solomatine, J. Valdes, eds.).

Papers in peer-reviewed journals

• Mazzoleni, M., Alfonso, L., Chacon-Hurtado, J., Solomatine, D. (2015). Assimilating uncertain, dynamic and intermittent streamflow observations in hydrological models. Adv. in Water Res., 83, 323-339 (Online on September 1, 2015). doi:10.1016/j.advwatres.2015.07.004.
• M. Mukolwe, K. Yan, G. Di Baldassarre, D.P. Solomatine (2015). Testing new sources of topographic data for flood propagation modelling under structural, parameter and observation uncertainty. Hydrol. Sci. J. doi: 10.1080/02626667.2015.1019507.
• N. Dogulu, P. Lopez Lopez, D. P. Solomatine, A. H. Weerts, and D. L. Shrestha (2015). Estimation of predictive hydrologic uncertainty using quantile regression and UNEEC methods and their comparison on contrasting catchments, Hydrol. Earth Syst. Sci., 19, 3181-3201. doi:10.5194/hess-19-3181-2015.
• Y.A. Bayissa, S.A. Moges, Y. Xuan, S.J. van Andel, S. Maskey, D.P. Solomatine, A. van Griensven, T. Tadesse (2015). Spatio-temporal assessment of meteorological drought under the influence of varying record length: the case of Upper Blue Nile Basin, Ethiopia, Hydrological Sci. J., doi:10.1080/02626667.2015.1032291.
• K. Yan, G. Di Baldassarre, D.P. Solomatine, G. J-P. Schumann (2015). A review of low-cost space-borne data for hydraulic modelling: topography, flood extent and water level. Hydrological Processes. doi:10.1002/hyp.10449.
• H.R. Maier, Z. Kapelan, J. Kasprzyk, J. Kollat, L.S. Matott, M.C. Cunha, G.C. Dandy, M.S. Gibbs, E. Keedwell, A. Marchi, A. Ostfeld, D. Savic, D.P. Solomatine, J.A. Vrugt, A.C. Zecchin, B.S. Minsker, E.J. Barbour, G. Kuczera, F. Pasha, A. Castelletti, M. Giuliani, P.M. Reed (2014). Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions, Environmental Modelling & Software, 62, 271-299 (doi:10.1016/j.envsoft.2014.09.013).
• Lopez Lopez, P.; Verkade, J. S.; Weerts, A. H.; Solomatine, D. P. (2014). Alternative configurations of quantile regression for estimating predictive uncertainty in water level forecasts for the upper Severn River: a comparison. Hydrol. Earth Syst. Sci., 18 (9), 3411-3428. doi:10.5194/hess-18-3411-2014.
• Shrestha, D.L., Kayastha, N., Solomatine, D., Price, R. (2014). Encapsulation of parametric uncertainty statistics by various predictive machine learning models: MLUE method. J Hydroinformatics, 16 (1), 95-113.
• Nasseri, M., Zahraie, B., Ajami, N.K., Solomatine, D.P. (2014). Monthly water balance modeling: Probabilistic, possibilistic and hybrid methods for model combination and ensemble simulation. Journal of Hydrology, 511, 675-691.
• Delipetrev B., Jonoski A., Solomatine D.P. (2014). Development of a web application for water resources based on open source software, Computers & Geosciences, 62, January 2014, 35-42.
• M. Nasseri, B. Zahraie, A. Ansari and D. P. Solomatine (2013). Uncertainty assessment of monthly water balance models based on Incremental Modified Fuzzy Extension Principle method. J. Hydroinformatics, 15(4), 1340–1360, doi: 10.2166/hydro.2013.159.
• Almoradie A., Jonoski A., Popescu I. and Solomatine D. (2013) Web-based access to water-related data using OGC WaterML 2.0. Int. J. of Advanced Computer Science and Applications (accepted).
• Almoradie, A., Jonoski, A., Stoica, F., Solomatine, D.P., Popescu, I. (2013). Web-based flood information system: case study of Somesul-Mare, Romania, J. Environmental Engineering and Management, 12(5), 1065-1070.
• V. Moya Quiroga, I. Popescu, D.P. Solomatine, L. Bociort (2013). Cloud and cluster computing in uncertainty analysis of integrated flood models. J. Hydroinformatics, 15(1), 55-69, online on 18 July 2012, doi:10.2166/hydro.2012.017.
• R. J. Abrahart, F. Anctil, P. Coulibaly, C. W. Dawson, N. J. Mount, L. M. See, A. Y. Shamseldin, D. P. Solomatine, E. Toth, R. L. Wilby. Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting. Progress in Physical Geography, August 2012, 36(4), 480-513, online on July 2012, doi: 10.1177/0309133312444943.
• Bhattacharya, B., van Kessel, T. and Solomatine, D.P. Spatio-temporal prediction of suspended sediment concentration in the coastal zone using artificial neural network and a numerical model. J. of Hydroinformatics, 14(3), 574-594, 2012.
• Gichamo Z., G., Popescu, I., Jonoski, A.,  Solomatine, D.P. River Cross Section Extraction from ASTER Global DEM for Flood Modeling, Environmental Modelling & Software, 31(5), 37-46, 2012.
• Di Baldassarre, G., Elshamy, M., van Griensven, A., Soliman, E., Kigobe, M., Ndomba, P., Mutemi, J., Mutua, F., Moges, S., Xuan, J.-Q., Solomatine, D. & Uhlenbrook, S. Future hydrology and climate in the River Nile basin: a review. Hydrol. Sci. J. 56(2), 2011, 199-211.
• Siek, M.B. and Solomatine, D.P. Real-time Data Assimilation for Chaotic Storm Surge Model Using NARX Neural Network. Journal of Coastal Research, SI 64, 1184-1188, 2011.
• Siek, M.B. and Solomatine, D.P. Optimized Dynamic Ensembles of Multiple Chaotic Models in Predicting Storm Surges. Journal of Coastal Research, SI 64, 1189-1194, 2011.
• M. Siek and D. P. Solomatine. Nonlinear chaotic model for predicting storm surges. Nonlinear Processes in Geophysics, 17, 405–420, 2010.
• K. Hassaballah, A. Jonoski, I. Popescu, D.P. Solomatine. Model-Based Optimization of Downstream Impact During Filling of a New Reservoir: Case Study of Mandaya/Roseires Reservoirs on the Blue Nile River. Water Resources Management, DOI: 10.1007/s11269-011-9917-8, 2011.
• Jung, N. C., Popescu, I., Price R. K., Solomatine,  D., Kelderman, P.,  Shin, J.K. (2011), The use of the A.G.P. test for determining the phytoplankton production and distribution in the thermally stratified reservoirs: The case of the Yongdam reservoir in Korea.  J. of Environmental Engineering and Management, 10 (11), 1647-1657.
• A. Elshorbagy, G. Corzo, S. Srinivasulu, and D.P. Solomatine. Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: Concepts and methodology. Hydrol. Earth Syst. Sci., 14, 1931–1941, 2010.
• A. Elshorbagy, G. Corzo, S. Srinivasulu, and D.P. Solomatine. Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application. Hydrol. Earth Syst. Sci., 14, 1943–1961, 2010.
• W. Barreto, Z. Vojinovic, R.K. Price, D.P. Solomatine. A multi-objective evolutionary approach to rehabilitation of urban drainage systems. ASCE Journal of Water Resources Planning and Management, 2010, doi: 10.1061/(ASCE)WR.1943-5452.0000070.
• L. Alfonso, A. Jonoski, D.P. Solomatine. Multi-objective optimisation of operational responses for contaminant flushing in water distribution networks. ASCE Journal of Water Resources Planning and Management, 136 (1), 2010, 48-58, doi: 10.1061/(ASCE)0733-9496(2010)136:1(48).
• Jung, N.C,  Popescu, I.,  Kelderman, P., Solomatine, D.P. and Price, R.K. Application of model trees and other machine learning techniques for algal growth prediction in Yongdam reservoir, Republic of Korea. J. of Hydroinformatics, 12(3), 2010, 262–274, doi:10.2166/hydro.2009.004.
• A. Zamani, A. Azimian, A. Heemink, D.P. Solomatine. Non-linear wave data assimilation with an ANN-type wind-wave model and Ensemble Kalman Filter (EnKF). Applied Mathematical Modelling (2009), doi:10.1016/j.apm.2009.10.013.
• G. Corzo, D. Solomatine, Hidayat, M. de Wit, M. Werner, S. Uhlenbrook, and R. Price. Combining semi-distributed process-based and data-driven models in flow simulation: a case study of the Meuse river basin. Hydrol. Earth Syst. Sci., 13, 1619–1634, 2009.
• D. L. Shrestha, N. Kayastha, and D. P. Solomatine. A novel approach to parameter uncertainty analysis of hydrological models using neural networks. Hydrol. Earth Syst. Sci., 13, 1235–1248, 2009.
• D.P. Solomatine, D.L. Shrestha. A novel method to estimate model uncertainty using machine learning techniques. Water Resources Res. 45, W00B11, doi:10.1029/2008WR006839, 2009.
• A. Zamani, A. Heemink, A. Azimian, D.P. Solomatine. Wave height prediction at Caspian Sea using data driven model and ensemble based data assimilation methods, J. Hydroinformatics, 2009, 11(2), 154–164.
• A. Zamani, D.P. Solomatine, A. Azimian, A. Heemink. Learning from data for wind–wave forecasting. Ocean Engineering, 2008, 35(10), 953-962.
• J. Hall, D.P. Solomatine. A framework for uncertainty analysis in flood risk management decisions. J. River Basin Management, 2008, 6(2), 85-98.
• D.L. Shrestha, D.P. Solomatine. Data-driven approaches for estimating uncertainty in rainfall-runoff modelling. J. River Basin Management, 2008, 6(2), 109-122.
• D.P. Solomatine and A. Ostfeld. Data-driven modelling: some past experiences and new approaches. J of Hydroinformatics, 2008, 10(1), 3-22.
• W. Nishida, D.P. Solomatine, M. Noguchi and S. Suzuki. Model parameter estimation by global optimization algorithm ACCO complemented by an ANN-based error estimator. JSCE Ann. J. of Hydraulic Engineering, 52, 2008, 1411-1416 (in Japanese).
• Fenicia, F., Solomatine, D. P., Savenije, H. H. G. and Matgen, P. Soft combination of local models in a multi-objective framework. Hydrol. Earth Syst. Sci., 11, 1797-1809, Special Issue “Data-driven approaches, optimization and model integration: hydrological applications”, R. Abrahart, L. See, D. Solomatine, and E. Toth (eds.), 2007.
• G. Corzo and D.P. Solomatine. Baseflow separation techniques for modular artificial neural network modelling in flow forecasting. Hydrological Sciences J., 2007, 52(3), 491-507.
• L. See, D.P. Solomatine, R. Abrahart, and E. Toth. Hydroinformatics: computational intelligence and technological developments in water science applications. Hydrological Sciences J., 2007, 52(3), 391-396.
• G. Corzo and D.P. Solomatine. Knowledge-based modularization and global optimization of artificial neural network models in hydrological forecasting. Neural Networks, 2007, 20, 528–536.
• D.P. Solomatine, M. Maskey, D.L. Shrestha. Instance-based learning compared to other data-driven methods in hydrologic forecasting. Hydrological Processes, 2008, 22, 275 – 287 (first published online: 24-July, 2007).
• Bhattacharya, B., Price, R.K., and D.P. Solomatine. A machine learning approach to modelling sediment transport, ASCE J. of Hydraulic Engineering, 2007, 133(4), 440-450.
• Vojinovic, Z.; Solomatine, D. and Price, R. K. Dynamic least-cost optimization of waste water systems remedial works requirements. Water, Science and Technology, 2006, 54(6-7), 467-475.
• D.L. Shrestha, D.P. Solomatine. Experiments with AdaBoost.RT, an Improved Boosting Scheme for Regression. Neural Computation, 2006, 17, 1678-1710.
• D.L. Shrestha, D.P. Solomatine. Machine learning approaches for estimation of prediction interval for the model output. Neural Networks J., 2006, 19(2), 225-235.
• D.P. Solomatine, M.B. Siek. Modular learning models in forecasting natural phenomena. Neural Networks J., 2006, 19(2), 215-224.
• B. Bhattacharya, D.P. Solomatine. Machine learning in sedimentation modeling. Neural Networks J., 2006, 19(2), 208-214.
• B. Bhattacharya, D.P. Solomatine. Machine learning in soil classification. Neural Networks J., 2006, 19(2), 186-195.
• V. Cherkassky, V. Krasnopolsky, D.P. Solomatine, J. Valdes. Computational Intelligence in Earth Sciences and Environmental Applications: Issues and Challenges. Neural Networks J., 2006, 19(2), 113-121.
• B. Bhattacharya, R.K. Price and D.P. Solomatine. Data-driven modelling in context to sediment transport. Journal of Physics and Chemistry of the Earth, Parts A/B/C, 30 (4-5), 2005, 297-302.
• W. Nishida, M. Noguchi, D.P. Solomatine. Study on Numerical Prediction of Tidal Current using Forecasted Weather Data. JSCE Ann. J. of Hydraulic Engineering, 2005, 49(2), 1279-1284 (in Japanese).
• B. Bhattacharya and D.P. Solomatine. Neural networks and M5 model trees in modelling water level-discharge relationship. Neurocomputing, 63, 2005, 381-396.
• A.H. Lobbrecht, Y. Dibike and D.P. Solomatine. Artificial Neural Networks and Fuzzy Systems in Model Based Control of the Overwaard Polder. ASCE Journal of Water Resources Planning and Management, 131(2), 2005, 135-145.
• D.P. Solomatine and Y. Xue. M5 model trees compared to neural networks: application to flood forecasting in the upper reach of the Huai River in China. ASCE Journal of Hydrologic Engineering, 9(6), 2004, 491-501.
• W. Nishida, M. Noguchi, H. Matsushita and D.P. Solomatine. A Study on the Application of Genetic Algorithm to Calibration of Water Quality Model. Ann. J. of Hydraulic Engineering, 48 (2), 2004, 1321-1326 (in Japanese).
• M. Noguchi, D.P. Solomatine, W. Nishida. Automatic Calibration of Water Quality Simulation Model Using Global Optimization Technique, Ann. J. of Hydraulic Engineering, 47 (2), 2003, 1267-1272 (in Japanese).
• B. Bhattacharya, A.H. Lobbrecht, D.P. Solomatine. Neural networks and reinforcement learning in control of water systems. ASCE Journal of Water Resources Planning and Management, vol. 129 (6), 2003, 458-465.
• D.P. Solomatine, K.N. Dulal. Model trees as an alternative to neural networks in rainfall–runoff modelling. Hydrological Sciences Journal, 48(3), 2003, 399-411.
• A.H. Lobbrecht, D.P. Solomatine. Machine learning in real-time control of water systems. Urban Water, 4, 2002, 283-289.
• S. Maskey, A. Jonoski, D.P. Solomatine. Groundwater remediation strategy using global optimization algorithms. ASCE Journal of Water Resources Planning and Management, 128 (6), 2002, 431-440.
• Y.B. Dibike, S. Velickov ., D.P. Solomatine and M.B. Abbott. Model induction with support vector machines: introduction and applications. ASCE Journal of Computing in Civil Engineering, 15(3), 2001, 208-216.
• Y.B. Dibike and D.P. Solomatine. River Flow Forecasting Using Artificial Neural Networks, Journal of Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 26(1), 2001, 1-8.
• A.J. Abebe, D.P. Solomatine, R. Venneker. Application of adaptive fuzzy rule-based models for reconstruction of missing precipitation events. Hydrological Sciences Journal, 45(3), 2000, 425-436.
• H. Yan, D.P. Solomatine, S. Velickov, M.B. Abbott. Distributed environmental impact assessment using Internet. Journal of Hydroinformatics, 1(1). 1999, 59-70.
• D.P. Solomatine, Y. Dibike, N. Kukuric. Automatic calibration of groundwater models using global optimization techniques. Hydrological Sciences Journal, 44(6), 1999, 879-894.
• Y.B. Dibike, D.P. Solomatine, M.B. Abbott. On the encapsulation of numerical-hydraulic models in artificial neural network. Journal of Hydraulic Research, 2, 1999, 147-161.
• D.P. Solomatine. Two strategies of adaptive cluster covering with descent and their comparison to other algorithms. Journal of Global Optimization, 14(1), 1999, 55-78.
• Y. Shen, D.P. Solomatine, H. van den Boogaard. Improving performance of chlorophyll concentration time series simulation with artificial neural networks. JSCE Annual Journal of Hydraulic Engineering, 42, 1998, February, 751-756.
• D.P. Solomatine. Object orientation in hydraulic modelling architectures. ASCE Journal of Computing in Civil Engineering, 10(2), 1996, 125-135.
• M.B. Abbott, D.P. Solomatine, A.W. Minns, A. Verwey, and W. van Nievelt. Education and training in hydroinformatics. Journal of Hydraulic Research, 32 (extra issue), 1994, 203-214.
• D.P. Solomatine. PC based tools for building decision support systems: analytical survey. Achievements and Perspectives (issue 46), Management and Progress of Science and Technology Series, No.8, Moscow: International Centre for Scientific and Technological Information, 1986 (in Russian).
• D.P. Solomatine. Application of structural modelling to regional development management. Achievements and Perspectives (issue 44), Regional Systems Series, No.4, Moscow: International Centre for Scientific and Technological Information, 1985, 115-119.
• I.A.Ganin,  D.P. Solomatine. Possibilities for formalization of computer models synthesis in management. - Achievements and Perspectives (issue 42), Management and Progress of Science and Technology Series, No.8 - Moscow: International Centre for Scientific and Technological Information, 1984, 80-89.

Chapters in books 

• K. Yan, J. Neal, D.P. Solomatine, G. Di Baldassarre (2014). Global and low-cost topographic data to support flood studies. Hydro-meteorological Hazards, Risks, and Disasters (Paron & Di Baldassarre, eds.). Elsevier (pp. 105-124).
• D. P. Solomatine and T. Wagener. Hydrological Modelling (Chapter 2.16). In: Treatise on Water Science (Wilderer, ed.), Volume 2: The Science of Hydrology, 435-457. Elsevier, 2011.
• Solomatine, D.P., Abrahart, R., See L. (2008). Data-driven modelling: concept, approaches, experiences. In: Practical Hydroinformatics: Computational Intelligence and Technological Developments in Water Applications (Abrahart, See, Solomatine, eds), Springer-Verlag.
• Solomatine, D.P., Vojinovic, Z.. (2008). Randomized search optimization algorithms and their application in rehabilitation of urban drainage systems. In: Practical Hydroinformatics: Computational Intelligence and Technological Developments in Water Applications (Abrahart, See, Solomatine, eds), Springer-Verlag.
• Solomatine, D.P. (2008). Committees of models in hydrologic modelling: boosting, mixtures and trees. In: Practical Hydroinformatics: Computational Intelligence and Technological Developments in Water Applications (Abrahart, See, Solomatine, eds), Springer-Verlag.
• B. Bhattacharya, I.K. Deibel, S.A.M. Karstens, D.P.Solomatine. Neural Networks in Sedimentation Modelling for the Approach Channel of the Port of Rotterdam. In: Estuarine and Coastal Fine Sediments Dynamics, volume 8, INTERCOH 2003, J. Maa, L. Sanford, D. Schoellhamer (eds.). Elsevier, 2006. 
• D.P. Solomatine. Data-driven modelling and computational intelligence methods in hydrology. Encyclopedia of Hydrological Sciences (M.G. Andersen, ed), vol. 1, John Wiley & Sons, 2005. 
• D.P. Solomatine. Applications of data-driven modelling and machine learning in control of water resources. In: Computational intelligence in control, M. Mohammadian, R.A. Sarker and X. Yao (eds.). Idea Group Publishing, 2002, pp. 197 – 217. 
• S. Velickov, D.P. Solomatine. Predictive data mining: practical examples. In: AI methods in Civil Engineering Applications (O. Schleider, A. Zijderveld, eds). Cottbus, 2000, pp. 3-19.
• S.Maskey, Y.B. Dibike, A.Jonoski, and D.P. Solomatine. Groundwater model approximation with artificial neural network for selecting optimal pumping strategy for plume removal, In: AI methods in Civil Engineering Applications (O. Schleider, A. Zijderveld, eds), Cottbus, 2000, pp. 67-80.
• Y.B. Dibike, S. Velickov, D.P. Solomatine. Support vector machines: review and applications in civil engineering. In: AI methods in Civil Engineering Applications (O. Schleider, A. Zijderveld, eds). Cottbus, 2000, p.45-58.
• D.P. Solomatine. Random search methods in model calibration and pipe network design. In: Water Industry Systems: Modelling and Optimization Applications, D. Savic, G. Walters (eds.). Research Studies Press Ltd., 1999, pp. 317-332.
• A.H. Lobbrecht, D.P. Solomatine. Control of water levels in polder areas using neural networks and fuzzy adaptive systems. In: Water Industry Systems: Modelling and Optimization Applications, D. Savic, G. Walters (eds.). Research Studies Press Ltd., 1999, pp. 509-518.
• V.A. Bogomolov, D.P. Solomatine. The possibilities of expert problem analysis in improving management effectiveness. In: The issues of improving the management of national economy (V.D. Rudashevsky, ed), pp. 90-97. - VNIISI, Moscow, 1987 (in Russian).
• V.A. Bogomolov, D.P. Solomatine, R.L. Sheinin. Dialogue software complex for analysis and modelling of organisational systems. In: The issues of organizational system functioning analysis (B.Z. Milner, R.L. Sheinin, eds), pp. 30-44. - VNIISI, Moscow, 1986 (in Russian).
• D.P. Solomatine. Implementation of decision support systems: the possibilities of using personal computers. - In: Systems and methods for decision support (S.V. Emeljanov, O.I. Larichev, eds), pp. 113-120. - VNIISI, Moscow, 1986 (in Russian).
• D.P. Solomatine. Issues and prospects for automating management processes. - In: Modern Issues of Information Technologies (D.S. Chereshkine, ed.). - VNIISI, Moscow, 1986 (in Russian).
• D.P. Solomatine, A.N. Shvetsov. Goals analysis of the management system in service sector. - In: Issues of organizational systems analysis (B. Milner and R. Sheinin, eds.), pp. 63-73. - VNIISI, Moscow, 1986 (in Russian).
• I.A.Ganin, A.I. Mishin, D.P. Solomatine. Problem structurization for regional management in a dialogue with a computer. - In: Methods of complex systems analysis, 116-122. - VNIISI, Moscow, 1984 (in Russian).
• I.A.Ganin, D.P. Solomatine. Structural models in inter-sectoral management: typology and problems of construction. - In: Intersectoral complexes: strategies for development and management (A.V.Kochetkov, ed.), 72-81. VNIISI, Moscow, 1983 (in Russian).
• K.G. Perfiljev, D.P. Solomatine, L.P. Victorov. Software for integrated system for interactive modelling of complex objects. - In: Software for optimization systems, 57-65. VNIISI, Moscow, 1982 (in Russian).
• A.A. Petrov, D.P. Solomatine. On possibilities of using interactive computer-based structural modelling methods in analysing the system of decisions in a national plan. - In: Methods of complex systems analysis, 115-122. - VNIISI, Moscow, 1981 (in Russian).
• D.P. Solomatine. Decisions in economic planning: computer modelling. - In: Regional management (A.V. Kochetkov, ed). - VNIISI, Moscow, 1981 (in Russian).
• I.A.Ganin, D.P. Solomatine. Complex systems structuring: psychological aspects. - In: Models and methods of systems studies, Part 3 (A. Danilov-Daniljan, ed.) – VNIISI, Moscow, 1981 (in Russian).
• I.A.Ganin, D.P. Solomatine. Large scale systems structural models: computer development. - In: Issues of management in technology, economics, biology, 129-134. - Nauka publishing house, Moscow, 1981 (in Russian).
• I.A.Ganin, D.P. Solomatine. Issues of building complex systems structures in a dialogue with a computer. - In: Issues of cybernetics and electronics, pp. 24-29 - Moscow, MAI, 1980 (in Russian).

Peer-reviewed conference papers

• K. Yan, F. Pappenberger, Y. M. Umer, D. P. Solomatine, G. Di Baldassarre (2014). Regional versus physically-based methods for flood inundation modelling in data scarce areas: an application to the Blue Nile. Proc. of the 11th Int. Conf. on Hydroinformatics, New York, USA.
• Solomatine D.P., Kuzmin V., Shrestha D.L. (2013). Learning errors of environmental mathematical models. Proc. Conf. on Engineering Applications of Neural Networks, 13-16 September 2013, Halkidiki, Greece.
• G. Di Baldassarre, G. Schumann, D. Solomatine, K. Yan, and P.D. Bates. 2012. Global flood mapping: current issues and future directions. Proc of the 10th Int. Conf. on Hydroinformatics, Hamburg, Germany.
• Kamel, A.M. Y., Bhattacharya, B., El Serafy, G. Y., van Kessel, T. and Solomatine, D.P. (2012). Uncertainty analysis of numerical models of fine sediment dynamics in the Dutch coastal zone. Proc of the 10th Int. Conf. on Hydroinformatics, Hamburg, Germany.
• Kayastha, N., Xuan, Y., Van Griensven, A., Solomatine, D.P. Identification of uncertainties in climate change impact on streamflows in the Nzoia catchment, Kenya. Proc of the 10th Int. Conf. on Hydroinformatics, Hamburg, Germany.
• Delipetrev B., Jonoski A., Solomatine D. (2012) Development of a Cloud Application for supporting Water Resources Modeling, Proc of the 10th Int. Conf. on Hydroinformatics, Hamburg, Germany.
• Di Baldassarre G., Schumann G., Solomatine D., Kun Y., and P.D. Bates. (2012). Global flood mapping: current issues and future directions. Proc of the 10th Int. Conf. on Hydroinformatics, Hamburg, Germany.
• Siek, M.B. and Solomatine, D.P. Nonlinear Multi-model Ensemble Prediction Using Dynamic Neural Network with Incremental Learning. Proc. IEEE International Joint Conferences on Neural Networks, San Jose, USA, July 2011 (Best Student Presentation Award).
• Siek, M.B. and Solomatine, D.P. Predicting Ocean Surge: Optimized Ensembles of Data Driven Chaos-based Models in Phase Space. Proc. 34th IAHR World Congress, Brisbane, Australia, July 2011. 
• B. Bhattacharya, S. Sewagudde, T. van Kessel and D.P. Solomatine. A hybrid approach in combining numerical and data-driven models in modelling fine sediment transport. Proc. 34th IAHR World Congress, Brisbane, Australia, 2011.
• M.B.A. Siek, D.P. Solomatine. Phase error correction for chaotic storm surge model. Proc. 9th Intern. Conf. on Hydroinformatics, Tianjin, China, September 2010. 
• M.B.A. Siek, D.P. Solomatine. A mixture of multi-models in phase space reconstruction. Proc. 9th Intern. Conf. on Hydroinformatics, Tianjin, China, September 2010. 
• D. L. Shrestha and D. P. Solomatine. Ranking of Pareto solutions in multi-objective model calibration and uncertainty analysis. Proc. 9th Intern. Conf. on Hydroinformatics, Tianjin, China, September 2010.
• V. Moya, I. Popescu, D.P. Solomatine. Monte carlo uncertainty analysis of hydraulic models using cloud computing. Proc. 9th Intern. Conf. on Hydroinformatics, Tianjin, China, September 2010.
• A. D. Santos Almoradie, A. Jonoski, Y. Xuan, T. Gichamo, D.P. Solomatine, J. De Ruiter. Web-based solutions for flood risk analysis, modelling and management. Proc. 9th Intern. Conf. on Hydroinformatics, Tianjin, China, September 2010.
• N. Kayastha, D. L. Shrestha and D. P. Solomatine. Experiments with several methods of parameter uncertainty estimation in hydrological modeling. Proc. 9th Intern. Conf. on Hydroinformatics, Tianjin, China, September 2010.
• Z. Xu, C. Velez, D.P. Solomatine, A. Lobbrecht. Use of cloud computing for optimal design of urban wastewater systems. Proc. 9th Intern. Conf. on Hydroinformatics, Tianjin, China, September 2010.
• D.P. Solomatine, L.A. Torres. Neural network approximation of a hydrodynamic model in optimizing reservoir operation. Proc. 2nd Intern. Conf. on Hydroinformatics, Zurich, September 1996, 201-206.
• PhD Thesis
• D.P. Solomatine Development and application of the interactive system for structural modelling in management. PhD Thesis, Institute for Systems Studies, Russian Academy of Sciences, Moscow, 1984 (in Russian).
• Online publications
• D.P. Solomatine (2012). An approach to multi-objective robust optimization allowing for explicit analysis of robustness (ROPAR). UNESCO-IHE. Online publication.
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